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A Survey of Edge Caching:Key Issues and Challenges

Tsinghua science and technology 2024年第3期29卷 818-842页

作者： Hanwen Li Mingtao Sun Fan Xia Xiaolong Xu Muhammad Bilal School of Computer Science Nanjing University of Information Science and TechnologyNanjing 210044China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 262700China Reading Academy Nanjing University of Information Science and TechnologyNanjing 210044China School of Software Nanjing University of Information Science and TechnologyNanjing 210044China Department of Computer Engineering Hankuk University of Foreign StudiesYongin-si 17035Republic of Korea

With the rapid development of mobile communication technology and intelligent applications,the quantity of mobile devices and data traffic in networks have been growing exponentially,which poses a great burden to networks and brings huge challenge to servicing user *** caching,which utilizes the storage and computation resources of the edge to bring resources closer to end users,is a promising way to relieve network burden and enhance user *** this paper,we aim to survey the edge caching techniques from a comprehensive and systematic *** first present an overview of edge caching,summarizing the three key issues regarding edge caching,i.e.,where,what,and how to cache,and then introducing several significant caching *** then carry out a detailed and in-depth elaboration on these three issues,which correspond to caching locations,caching objects,and caching strategies,*** particular,we innovate on the issue“what to cache”,interpreting it as the classification of the“caching objects”,which can be further classified into content cache,data cache,and service ***,we discuss several open issues and challenges of edge caching to inspire future investigations in this research area.

关键词： edge caching edge computing caching location caching object caching strategy 5G networkarchitecture Internet of Things(IoT)

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Named Entity Recognition in Electronic Medical Records Incorporating Pre-trained and Multi-Head Attention

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第4期51卷 401-408页

作者： Yang, Haotian Wang, Li Yang, Yanpeng School of Computer Science and Software Engineering University of Science and Technology Liaoning CO Anshan114051 China College of Computer Science and Software Engineering University of Science and Technology Liaoning CO Anshan114051 China Network Information Centre University of Science and Technology Liaoning CO Anshan114051 China

Chinese Named Entity Recognition (NER) for Electronic Medical Records (EMR) is a fundamental task in building a digital hospital and is widely considered to be a sequence annotation problem in the Natural Language Processing domain. (NLP). However, existing deep learning sequence annotation models cannot fully use the large amount of unannotated data for Chinese EMRs that contain a vast number of professional unregistered words, named entities, and inter-of-entity relationships carrying rich professional knowledge. Moreover, the syntactic structure of EMR sentences is complex, and the text is long;the features of the EMR documents often cannot be captured deeply. Aiming at these two problems, this paper proposes a deep learning method that combines Multi-Head Attention with a pre-trained language model. (BERT-BiGRU-Att-CRF). The method uses the BERT pre-trained model to obtain dynamic word vectors combined with contextual information, extracts global semantic features through a Bidirectional Gated Recurrent Unit (BiGRU), obtains augmented semantic features by using the Multi-Head Attention. Finally, using Conditional Random Field (CRF) decoding, outputs the globally optimal label sequence with greatest probability. The model is trained using the CCKS2019 Chinese EMR dataset containing six types of entities: anatomical sites, surgeries, diseases and diagnoses, medicines, laboratory tests, and imaging tests, and good results are achieved with an F1 score of 86.97%. © (2024) International Association of Engineers.

关键词： Random processes

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Insulator defect detection under extreme weather based on synthetic weather algorithm and improved YOLOv7

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High Voltage 2025年第1期10卷 69-77页

作者： Yong Yang Shuai Yang Chuan Li Yunxuan Wang Xiaoqian Pi Yuxin Lu Ruohan Wu State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical Engineering and ElectronicsHuazhong University of Science and TechnologyWuhanChina State Grid Hunan Electric Power Company ChangshaChina

Efficient and accurate insulator defect detection is essential for maintaining the safe and stable operation of transmission ***,the detection effectiveness is adversely impacted by complex and changeable environmental backgrounds,particularly under extreme weather that elevates accident ***,this research proposes a high-precision intelligent strategy based on the synthetic weather algorithm and improved YOLOv7 for detecting insulator defects under extreme *** proposed meth-odology involves augmenting the dataset with synthetic rain,snow,and fog algorithm ***,the original dataset undergoes augmentation through affine and colour transformations to improve model's generalisation performance under complex power inspection *** achieve higher recognition accuracy in severe weather,an improved YOLOv7 algorithm for insulator defect detection is proposed,integrating focal loss with SIoU loss function and incorporating an optimised decoupled head *** results indicate that the synthetic weather algorithm processing significantly improves the insulator defect detection accuracy under extreme weather,increasing the mean average precision by 2.4%.Furthermore,the authors’improved YOLOv7 model achieves 91.8%for the mean average precision,outperforming the benchmark model by 2.3%.With a detection speed of 46.5 frames per second,the model meets the requirement of real-time detection of insulators and their defects during power inspection.

关键词： weather extreme algorithm

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RDP: Ranked Differential Privacy for Facial Feature Protection in Multiscale Sparsified Subspaces

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IEEE Internet of Things Journal 2025年第11期12卷 17333-17348页

作者： Ou, Lu Liao, Shaolin Gao, Shihui Huang, Guandong Qin, Zheng Hunan University School of Journalism and Communication Hunan Changsha410082 China Sun Yat-sen University School of Electronics and Information Technology Guangzhou510275 China Illinois Institute of Technology Department of Electrical and Computer Engineering ChicagoIL60616 United States Purdue University Elmore School of Electrical and Computer Engineering West LafayetteIN47907 United States Hunan University College of Computer Science and Electronic Engineering Hunan Changsha410082 China State Grid Hunan Materials Company Department of Deployment Management Hunan Changsha410118 China

With the widespread sharing of personal face images on the Internet, systems based on face recognition encounter the real threat of being breached by potential adversaries who are able to access individuals’ face images and use them to intrude the systems. In this article, we propose a novel privacy protection method in the sparsified multiscale feature subspaces to protect sensitive facial features, taking care of the influence or weight-ranked subspace coefficients on the privacy budget, named "ranked differential privacy (RDP)." After the multiscale subspaces’ decomposition, the lightweight Laplacian noise is added to the dimension-reduced sparsified subspaces’ coefficients according to the geometric superposition method. Then, we rigorously prove that the RDP satisfies Ε0-differential privacy. After that, the nonlinear Lagrange multiplier method (LM) is formulated for the constraint optimization problem of maximizing the utility of protected face images of high-visualization quality with sanitizing noise, under a given privacy budget Ε0. Then, two methods are proposed to solve the nonlinear Lagrangian multiplier method (LM) problem and obtain the optimal noise scale parameters: 1) the analytical normalization approximation (NA) method with identical average noise scale parameter for real-time online applications and 2) the LM optimization method via gradient descent (LMGD) to obtain the nonlinear solution through iterative updating for more accurate offline applications. Experimental results on two real-world datasets show that our proposed RDP outperforms other state-of-the-art methods: at a privacy budget of Ε0 = 0.2 , the peak signal-to-noise ratio (PSNR) of the optimized RDP is about ~10 dB higher than (10 times as high as) the highest PSNR of all state-of-the-art methods compared. © 2014 IEEE.

关键词： Differential privacy

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FLAG: frequency-based local and global network for face forgery detection

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Multimedia Tools and Applications 2025年第2期84卷 647-663页

作者： Zhou, Kai Sun, Guanglu Wang, Jun Wang, Jiahui Yu, Linsen School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China Department of Information Engineering and Mathematics University of Siena Siena53100 Italy

Deepfake detection aims to mitigate the threat of manipulated content by identifying and exposing forgeries. However, previous methods primarily tend to perform poorly when confronted with cross-dataset scenarios. To address the above issue, we propose an innovative hybrid network called the Frequency-based Local and Global (FLAG) network to explore local and global information with the help of frequency-domain cues for better generalization capability. In consideration of the fact that forged faces often exhibit flaws in the frequency domain, we design a Frequency-based Attention Enhancement Module (FAEM) to enhance the aggregation of CNN and Vision Transformer (ViT). In this design, local features from CNN are attentively enhanced by selected frequency coefficients in FAEM, facilitating generalizable global features learning by the ViT module. The effectiveness of the proposed method is validated via numerous experiments and the generalization performance is improved under cross-dataset scenarios. Especially, the proposed method have obtained an AUC of 99.26% and an ACC of 96.56% using intra-dataset experimental results on FaceForensics++ (C23). © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Face recognition

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Learning-based tracking control of AUV:Mixed policy improvement and game-based disturbance rejection

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CAAI Transactions on Intelligence technology 2025年第2期10卷 510-528页

作者： Jun Ye Hongbo Gao Manjiang Hu Yougang Bian Qingjia Cui Xiaohui Qin Rongjun Ding State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle College of Mechanical and Vehicle EngineeringHunan UniversityChangshaChina School of Information Science and Technology and Institute of Advanced TechnologyUniversity of Science and Technology of ChinaHefeiChina Nanyang Technological University SingaporeSingapore Wuxi Intelligent Control Research Institute of Hunan University Hunan UniversityWuxiChina

A mixed adaptive dynamic programming(ADP)scheme based on zero-sum game theory is developed to address optimal control problems of autonomous underwater vehicle(AUV)systems subject to disturbances and safe *** combining prior dynamic knowledge and actual sampled data,the proposed approach effectively mitigates the defect caused by the inaccurate dynamic model and significantly improves the training speed of the ADP ***,the dataset is enriched with sufficient reference data collected based on a nominal model without considering modelling ***,the control object interacts with the real environment and continuously gathers adequate sampled data in the *** comprehensively leverage the advantages of model-based and model-free methods during training,an adaptive tuning factor is introduced based on the dataset that possesses model-referenced information and conforms to the distribution of the real-world environment,which balances the influence of model-based control law and data-driven policy gradient on the direction of policy *** a result,the proposed approach accelerates the learning speed compared to data-driven methods,concurrently also enhancing the tracking performance in comparison to model-based control ***,the optimal control problem under disturbances is formulated as a zero-sum game,and the actor-critic-disturbance framework is introduced to approximate the optimal control input,cost function,and disturbance policy,***,the convergence property of the proposed algorithm based on the value iteration method is ***,an example of AUV path following based on the improved line-of-sight guidance is presented to demonstrate the effectiveness of the proposed method.

关键词： adaptive dynamic programming autonomous underwater vehicle game theory optimal control reinforcement learning

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Multi-Modal Emotion Recognition Network with Balanced Audio-Visual Feature Extraction 5

Multi-Modal Emotion Recognition Network with Balanced Audio-...

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5th International Conference on Artificial Intelligence and computer engineering, ICAICE 2024

作者： Chen, Zeyu Wu, Yiming Cao, Ronghui Hunan University College of Computer Science and Electronic Engineering Changsha China Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China

ISBN: (纸本)9798331528911

Video and audio are ways for humans to perceive the world beyond language. It is meant to enable robots to imitate and recognize human emotional expressions. However, most current audio-visual emotion analysis models tend to extract deep features from only one modality. In contrast, the other modality plays a supporting role and cannot fully extract deep features from both modalities. This article proposes a balanced audio-visual emotion analysis model. Specifically, EfficientNet and Wav2vec 2.0 are used for visual and auditory modality feature extraction, respectively, ensuring that indepth features can be extracted from both modalities. Secondly, we use Transformer as the decision-level fusion operator, exchanging information between the two modalities. We verified our model on the RAVDESS dataset and achieved a Top1 accuracy of 88.54%, surpassing audio-visual emotion analysis models of the auxiliary type. © 2024 IEEE.

关键词： Emotion Recognition

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Fixed-time command filtered output feedback control for twin-roll inclined casting system with prescribed performance

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Mathematical Biosciences and engineering 2024年第2期21卷 2282-2301页

作者： Gao, Dongxiang Zhang, Yujun Wu, Libing Liu, Sihan School of Computer and Software Engineering University of Science and Technology Liaoning Liaoning CO Anshan114051 China School of Science University of Science and Technology Liaoning Liaoning CO Anshan114051 China

The article investigates the issue of fixed-time control with adaptive output feedback for a twin-roll inclined casting system (TRICS) with disturbance. First, by using the mean value theorem, the nonaffine functions are decoupled to simplify the system. Second, radial basis function neural networks (RBFNNs) are introduced to approximate an unknown term, and a nonlinear neural state observer is created to handle the effects of unmeasured states. Then, the backstepping design framework is combined with prescribed performance and command filtering techniques to demonstrate that the scheme proposed in this article guarantees system performance within a fixed-time. The control design parameters determine the upper bound of settling time, regardless of the initial state of the system. Meanwhile, it ensures that all signals in the closed-loop system (CLS) remain bounded, and it can also maintain the tracking error within a predefined range within a fixed time. Finally, simulation results assert the effectiveness of the method. © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License.

关键词： Closed loop systems

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Class-conditional domain adaptation for semantic segmentation

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Computational Visual Media 2024年第5期10卷 1013-1030页

作者： Yue Wang Yuke Li James H.Elder Runmin Wu Huchuan Lu School of Information and Communication Engineering Dalian University of TechnologyDalian 116024China School of Computer Science Wuhan UniversityWuhan 430072China Department of Electrical Engineering and Computer Science York UniversityToronto M3J 1P3Canada Department of Computer Science the University of Hong KongHong Kong 999077China

Semantic segmentation is an important sub-task for many ***,pixel-level ground-truth labeling is costly,and there is a tendency to overfit to training data,thereby limiting the generalization *** domain adaptation can potentially address these problems by allowing systems trained on labelled datasets from the source domain(including less expensive synthetic domain)to be adapted to a novel target *** conventional approach involves automatic extraction and alignment of the representations of source and target domains *** limitation of this approach is that it tends to neglect the differences between classes:representations of certain classes can be more easily extracted and aligned between the source and target domains than others,limiting the adaptation over all ***,we address:this problem by introducing a Class-Conditional Domain Adaptation(CCDA)*** incorporates a class-conditional multi-scale discriminator and class-conditional losses for both segmentation and ***,they measure the segmentation,shift the domain in a classconditional manner,and equalize the loss over *** results demonstrate that the performance of our CCDA method matches,and in some cases,surpasses that of state-of-the-art methods.

关键词： domain adaptation generative adversarial networks semantic segmentation cityscapes

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Quantifying Bytes:Understanding Practical Value of Data Assets in Federated Learning

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Tsinghua science and technology 2025年第1期30卷 135-147页

作者： Minghao Yao Saiyu Qi Zhen Tian Qian Li Yong Han Haihong Li Yong Qi School of Computer Science and Technology Xi’an Jiaotong UniversityXi’an 710049China School of Cyber Science and Engineering Xi’an Jiaotong UniversityXi’an 710049China

The data asset is emerging as a crucial component in both industrial and commercial *** valuable knowledge from the data benefits decision-making and ***,the usage of data assets raises tension between sensitive information protection and value *** an emerging machine learning paradigm,Federated Learning(FL)allows multiple clients to jointly train a global model based on their data without revealing *** approach harnesses the power of multiple data assets while ensuring their *** the benefits,it relies on a central server to manage the training process and lacks quantification of the quality of data assets,which raises privacy and fairness *** this work,we present a novel framework that combines Federated Learning and Blockchain by Shapley value(FLBS)to achieve a good trade-off between privacy and ***,we introduce blockchain in each training round to elect aggregation and evaluation nodes for training,enabling decentralization and contribution-aware incentive distribution,with these nodes functionally separated and able to supervise each *** experimental results validate the effectiveness of FLBS in estimating contribution even in the presence of heterogeneity and noisy data.

关键词： Federated Learning(FL) blockchain fairness

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